沙特股票价格趋势的有效短期预测使用技术指标和大型多变量时间序列
1Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
PeerJ. Computer science
|June 22, 2023
概括
本研究介绍了用于准确的沙特股票价格短期预测的Gated Recurrent Unit (GRU) 模型. 与现有的方法相比,该方法提高了沙特股票市场趋势预测.
科学领域:
- 计算金融是指计算金融.
- 机器学习应用 机器学习应用
- 时间序列分析时间序列分析
背景情况:
- 由于波动性和非线性,股票市场预测是复杂的.
- 准确的短期预测对于积极的决策至关重要.
- 现有的研究缺乏对沙特股票市场短期预测的全面方法.
研究的目的:
- 开发和验证一种新的方法来准确预测沙特股票价格和趋势的短期预测.
- 为了弥补沙特股票市场特定动态的可参考工作的差距.
- 利用先进的循环神经网络技术进行多变量时间序列预测.
主要方法:
- 开发了一种定制功能工程管道,用于预处理原始库存数据并生成财务技术指标.
- 使用基于步骤的滑动窗口方法来创建多变量时间序列数据.
- 在沙特股票市场指数 (TASI) 数据上训练,为多步预测设计和校准了一个门式循环单位 (GRU) 模型.
- 该GRU模型与一个带有外源回归器的向量自回归移动平均值 (VARMAX) 模型进行了基准测试.
主要成果:
- 拟议的GRU模型在沙特股价趋势的短期预测方面表现出卓越的准确性.
- 经验结果显示,与VARMAX基线模型相比,GRU方法的性能优越.
- 基于回归的指标证实了开发的预测方法的准确性和可靠性.
结论:
- 开发的基于GRU的方法为沙特股票市场短期预测提供了全面而准确的方法.
- 这项研究为预测沙特股价趋势提供了有价值的参考资料,其表现优于传统方法.
- 这些发现强调了先进的深度学习模型在捕捉复杂的金融市场动态方面的潜力.
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